title: Automated detection of sleep apnea in infants using minimally invasive sensors.
abstract: To address the difficult and necessity of early detection of sleep apnea hypopnea syndrome in infants, we present a study into the effectiveness of pulse oximetry as a minimally invasive means of automated diagnosis of sleep apnea in infants. Overnight polysomnogram data from 328 infants were used to extract time-domain based oximetry features and scored arousal data for each subject. These records were then used to determine apnea events and to train a classifier model based on linear discriminants. Performance of the classifier was evaluated using a leave-one-out cross-validation scheme and an accuracy of 68% was achieved, with a specificity of 68.6% and a sensitivity of 55.9%.
authors.forename: Gregory
authors.initials: G
authors.lastname: Cohen
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
mesh_terms: D016208:Databases, Factual; D016002:Discriminant Analysis; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D010092:Oximetry; D017286:Polysomnography; D047928:Premature Birth; D012680:Sensitivity and Specificity; D035781:Siblings; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*